基于索赔的算法在美国孕妇中识别孕前糖尿病的验证。
Validation of a Claims-based Algorithm to Identify Pregestational Diabetes Among Pregnant Women in the United States.
机构信息
From the Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston.
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine Brigham and Women's Hospital and Harvard Medical School, Boston.
出版信息
Epidemiology. 2021 Nov 1;32(6):855-859. doi: 10.1097/EDE.0000000000001397.
BACKGROUND
Identifying pregestational diabetes in pregnant women using administrative claims databases is important for studies of the safety of antidiabetic treatment in pregnancy, but limited data are available on the validity of case-identifying algorithms. The purpose of this study was to evaluate the validity of an administrative claims-based algorithm to identify pregestational diabetes.
METHODS
Using a cohort of pregnant women nested within the Medicaid Analytic Extract (MAX) database, we developed an algorithm to identify pregestational type 1 and type 2 diabetes, distinct from gestational diabetes. Within a single large healthcare system in the Boston area, we identified women who delivered an infant between 2000 and 2010 and were covered by Medicaid, and linked their electronic health records to their Medicaid claims within MAX. Medical records were reviewed by two physicians blinded to the algorithm classification to confirm or rule out pregestational diabetes, with disagreements resolved by discussion. We calculated positive predictive values with 95% confidence intervals using the medical record as the reference standard.
RESULTS
We identified 49 pregnancies classified by the claims-based algorithm as pregestational diabetes that were linked to the electronic health records and had records available for review. The PPV for any pregestational diabetes was 92% [95% confidence interval (CI) 82%, 97%], type 2 diabetes 87% (68%, 95%), and type 1 diabetes 57% (37%, 75%).
CONCLUSIONS
The claims-based algorithm for pregestational diabetes and type 2 diabetes performed well; however, the PPV was low for type 1 diabetes.
背景
利用行政索赔数据库识别孕妇的孕前糖尿病对于研究妊娠期间抗糖尿病治疗的安全性非常重要，但关于病例识别算法的有效性的数据有限。本研究的目的是评估基于行政索赔的算法识别孕前糖尿病的有效性。
方法
我们使用嵌套在 Medicaid Analytic Extract（MAX）数据库中的孕妇队列，开发了一种算法来识别与妊娠期糖尿病不同的孕前 1 型和 2 型糖尿病。在波士顿地区的单一大型医疗保健系统中，我们确定了在 2000 年至 2010 年间分娩并由 Medicaid 覆盖的妇女，并将其电子健康记录与其 MAX 中的 Medicaid 索赔联系起来。两名医生对病历进行了审查，他们对算法分类是盲的，以确认或排除孕前糖尿病，有分歧的通过讨论解决。我们使用病历作为参考标准，计算了阳性预测值及其 95%置信区间。
结果
我们确定了 49 例由索赔算法分类为孕前糖尿病的妊娠，这些妊娠与电子健康记录相关联，并且有记录可供审查。任何孕前糖尿病的阳性预测值为 92%（95%置信区间 82%，97%），2 型糖尿病为 87%（68%，95%），1 型糖尿病为 57%（37%，75%）。
结论
用于识别孕前糖尿病和 2 型糖尿病的基于索赔的算法表现良好；然而，1 型糖尿病的阳性预测值较低。
Zotero 插件